Abstract
Intuitive geometric interpretation for Support Vector Machines (SVM) provides an alternative way to implement SVM. Although Bennet’s nearest point algorithm (NPA) can deal with reduced convex hulls, it has some disadvantages. In the paper, a feasible direction explanation for NPA is proposed so that computation of kernel can be reduced greatly. Besides, the original NPA is extended to handle the arbitrary valid value of μ, therefore a better generalization performance may be obtained.
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Li, J., Zhang, J., Zhang, B., Lin, F. (2004). Improvements to Bennett’s Nearest Point Algorithm for Support Vector Machines. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_77
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DOI: https://doi.org/10.1007/978-3-540-28647-9_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
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